Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges

Muhammad Usama, Junaid Qadir, Aunn Raza, Hunain Arif, Kok Lim Alvin Yau, Yehia Elkhatib, Amir Hussain, Ala Al-Fuqaha*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

303 Citations (Scopus)

Abstract

While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning. Recently, there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services, such as traffic engineering, anomaly detection, Internet traffic classification, and quality of service optimization. The growing interest in applying unsupervised learning techniques in networking stems from their great success in other fields, such as computer vision, natural language processing, speech recognition, and optimal control (e.g., for developing autonomous self-driving cars). In addition, unsupervised learning can unconstrain us from the need for labeled data and manual handcrafted feature engineering, thereby facilitating flexible, general, and automated methods of machine learning. The focus of this survey paper is to provide an overview of applications of unsupervised learning in the domain of networking. We provide a comprehensive survey highlighting recent advancements in unsupervised learning techniques, and describe their applications in various learning tasks, in the context of networking. We also provide a discussion on future directions and open research issues, while identifying potential pitfalls. While a few survey papers focusing on applications of machine learning in networking have previously been published, a survey of similar scope and breadth is missing in the literature. Through this timely review, we aim to advance the current state of knowledge, by carefully synthesizing insights from previous survey papers, while providing contemporary coverage of the recent advances and innovations.

Original languageEnglish
Article number8713992
Pages (from-to)65579-65615
Number of pages37
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 2019

Keywords

  • Machine learning
  • computer networks
  • deep learning
  • unsupervised learning

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